import numpy as np
import platform
import pickle
import os
import matplotlib.pyplot as plt
import time


# 加载数据
def load_pickle(f):
    version = platform.python_version_tuple()
    if version[0] == '2':
        return pickle.load(f)
    elif version[0] == '3':
        return pickle.load(f, encoding='latin1')
    raise ValueError("invalid python version: {}".format(version))


def loadCIFAR_batch(filename):
    with open(filename, 'rb') as f:
        datadict = load_pickle(f)
        x = datadict['data']
        y = datadict['labels']
        x = x.reshape(10000, 3, 32, 32).transpose(0, 3, 2, 1).astype('float')
        y = np.array(y)
        return x, y


def loadCIFAR10(root):
    xs = []
    ys = []
    for b in range(1, 6):
        f = os.path.join(root, 'data_batch_%d' % (b,))
        x, y = loadCIFAR_batch(f)
        xs.append(x)
        ys.append(y)
    X = np.concatenate(xs)
    Y = np.concatenate(ys)
    x_test, y_test = loadCIFAR_batch(os.path.join(root, 'test_batch'))
    return X, Y, x_test, y_test


# 将数据分成train和test，以及显示
def data_validation(x_train, y_train, x_test, y_test):
    num_training = 49000
    num_validation = 1000
    num_test = 1000
    num_dev = 500
    mean_image = np.mean(x_train, axis=0)
    x_train -= mean_image
    mask = range(num_training, num_training + num_validation)
    X_val = x_train[mask]
    Y_val = y_train[mask]
    mask = range(num_training)
    X_train = x_train[mask]
    Y_train = y_train[mask]
    mask = np.random.choice(num_training, num_dev, replace=False)
    X_dev = x_train[mask]
    Y_dev = y_train[mask]
    mask = range(num_test)
    X_test = x_test[mask]
    Y_test = y_test[mask]
    X_train = np.reshape(X_train, (X_train.shape[0], -1))
    X_val = np.reshape(X_val, (X_val.shape[0], -1))
    X_test = np.reshape(X_test, (X_test.shape[0], -1))
    X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))
    X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])
    X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])
    X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])
    X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])
    return X_val, Y_val, X_train, Y_train, X_dev, Y_dev, X_test, Y_test


def showPicture(x_train, y_train):
    classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
    num_classes = len(classes)
    samples_per_classes = 7
    for y, cls in enumerate(classes):
        idxs = np.flatnonzero(y_train == y)
        idxs = np.random.choice(idxs, samples_per_classes, replace=False)
        for i, idx in enumerate(idxs):
            plt_index = i * num_classes + y + 1
            plt.subplot(samples_per_classes, num_classes, plt_index)
            plt.imshow(x_train[idx].astype('uint8'))
            plt.axis('off')
            if i == 0:
                plt.title(cls)
    plt.show()


showPicture(loadCIFAR10("./data1/cifar-10-batches-py")[0], loadCIFAR10("./data1/cifar-10-batches-py")[1])


# SVM算法
class LinearSVM():
    def __init__(self):
        self.W = None

    def train(self, X, y, learning_rate=1e-3, reg=1e-5, num_iters=100, batch_size=200, verbose=False):
        num_train, dim = X.shape
        num_classes = np.max(y) + 1
        if self.W is None:
            self.W = 0.001 * np.random.randn(dim, num_classes)

        loss_history = []  # 记录每次的loss
        for it in range(num_iters):
            X_batch = None
            y_batch = None
            idx_batch = np.random.choice(num_train, batch_size, replace=True)
            X_batch = X[idx_batch]
            y_batch = y[idx_batch]
            # 计算梯度和损失值
            loss, grad = self.loss(X_batch, y_batch, reg)
            loss_history.append(loss)
            self.W -= learning_rate * grad
            if verbose and it % 100 == 0:
                print('iteration %d / %d: loss %f' % (it, num_iters, loss))
        return loss_history

    def loss(self, x, y, reg):
        loss = 0.0
        dw = np.zeros(self.W.shape)
        num_train = x.shape[0]
        scores = x.dot(self.W)
        correct_class_score = scores[range(num_train), list(y)].reshape(-1, 1)
        margin = np.maximum(0, scores - correct_class_score + 1)
        margin[range(num_train), list(y)] = 0
        loss = np.sum(margin) / num_train + 0.5 * reg * np.sum(self.W * self.W)

        num_classes = self.W.shape[1]
        inter_mat = np.zeros((num_train, num_classes))
        inter_mat[margin > 0] = 1
        inter_mat[range(num_train), list(y)] = 0
        inter_mat[range(num_train), list(y)] = -np.sum(inter_mat, axis=1)

        dW = (x.T).dot(inter_mat)
        dW = dW / num_train + reg * self.W
        return loss, dW

    def predict(self, X):
        y_pred = np.zeros(X.shape[0])
        scores = X.dot(self.W)
        y_pred = np.argmax(scores, axis=1)
        return y_pred


# 测试代码
svm = LinearSVM()
tic = time.time()
cifar10_name = "./data1/cifar-10-batches-py"
x_train, y_train, x_test, y_test = loadCIFAR10(cifar10_name)
X_val, Y_val, X_train, Y_train, X_dev, Y_dev, X_test, Y_test = data_validation(x_train, y_train, x_test, y_test)
loss_hist = svm.train(X_train, Y_train, learning_rate=1e-7, reg=2.5e4,
                      num_iters=3000, verbose=True)

# loss_hist = svm.fit(X_train,Y_train)
toc = time.time()
print('That took %fs' % (toc - tic))
plt.plot(loss_hist)
plt.xlabel('Iteration number')
plt.ylabel('Loss value')
plt.show()
y_test_pred = svm.predict(X_test)
test_accuracy = np.mean(Y_test == y_test_pred)
print('accuracy: %f' % test_accuracy)
w = svm.W[:-1, :]  # strip out the bias
w = w.reshape(32, 32, 3, 10)
w_min, w_max = np.min(w), np.max(w)
classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
# 绘制各个种类的权重图
for i in range(10):
    plt.subplot(2, 5, i + 1)
    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)
    plt.imshow(wimg.astype('uint8'))
    plt.axis('off')
    plt.title(classes[i])
plt.show()